hdinsight-migration

Update Check — ONCE PER SESSION (mandatory) The first time this skill is used in a session, run the check-updates skill before proceeding.

• GitHub Copilot CLI / VS Code: invoke the check-updates skill.
• Claude Code / Cowork / Cursor / Windsurf / Codex: compare local vs remote package.json version.
• Skip if the check was already performed earlier in this session.

CRITICAL NOTES

1. To find workspace details (including its ID) from a workspace name: list all workspaces, then use JMESPath filtering
2. To find item details (including its ID) from workspace ID, item type, and item name: list all items of that type in that workspace, then use JMESPath filtering
3. HDInsight has no mssparkutils or dbutils equivalent — notebookutils is net-new capability being introduced
4. HiveContext and SQLContext are legacy Spark 1.x/2.x APIs — Fabric uses Spark 3.x SparkSession exclusively
5. wasb:// paths are deprecated and require a Storage Account key or SAS — replace with OneLake shortcuts

HDInsight → Microsoft Fabric Migration

Prerequisite Knowledge

Read these companion documents before executing migration tasks:

• COMMON-CORE.md — Fabric REST API patterns, authentication, token audiences, item discovery
• COMMON-CLI.md — az rest, az login, token acquisition, Fabric REST via CLI
• SPARK-AUTHORING-CORE.md — Notebook deployment, lakehouse creation, Spark job execution

For notebook and Lakehouse creation, see spark-authoring-cli. For Fabric Warehouse DDL/DML authoring, see sqldw-authoring-cli.

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Table of Contents

Topic	Reference
Migration Workload Map	§ Migration Workload Map
SparkSession & Context API Changes	§ SparkSession API Changes
WASB / ABFS → OneLake Path Migration	path-migration.md
Hive DDL → Delta Lake / Lakehouse Schemas	hive-to-delta.md
Oozie → Fabric Pipelines	§ Oozie → Fabric Pipelines
Introducing notebookutils	§ Introducing notebookutils
Before/After Code Patterns	code-patterns.md
Spark Configuration Differences	§ Spark Configuration Differences
Must / Prefer / Avoid	§ Must / Prefer / Avoid
Authentication & Token Acquisition	COMMON-CORE.md § Authentication
Lakehouse Management	SPARK-AUTHORING-CORE.md § Lakehouse Management

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Migration Workload Map

HDInsight Component	Fabric Target	Notes
Spark cluster (notebooks, scripts)	Fabric Spark (Lakehouse / Notebooks / SJD)	No persistent cluster — Starter Pool or Custom Pool provides on-demand Spark
Hive / HiveServer2	Lakehouse SQL Endpoint + Lakehouse schemas	Delta Lake replaces Hive metastore; schemas provide namespace equivalent
HBase	Fabric Warehouse or Azure Cosmos DB (separate from Fabric)	HBase has no direct Fabric equivalent — assess workload access patterns
Oozie workflows	Fabric Data Pipelines	Map Oozie actions to Fabric activities; see § Oozie → Fabric Pipelines
YARN Resource Manager	Fabric Spark monitoring (Spark UI, Monitoring Hub)	No YARN — Fabric manages compute automatically
Ambari	Fabric Monitoring Hub + Admin Portal	Cluster health, capacity, and job monitoring
WASB / ABFS storage	OneLake Shortcuts → abfss://workspace@onelake.dfs.fabric.microsoft.com/	See path-migration.md
Ranger policies	Fabric workspace roles + OneLake data access roles	Map Ranger row/column filters to Lakehouse row-level security
Livy REST server	Fabric Livy API	Compatible endpoint — see SPARK-AUTHORING-CORE.md

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SparkSession & Context API Changes

HDInsight Spark clusters often use legacy Spark 1.x / 2.x API styles. Replace all of these with the unified SparkSession:

Legacy HDInsight Pattern	Fabric Spark 3.x Replacement
from pyspark import SparkContext; sc = SparkContext()	Not needed — sc = spark.sparkContext (pre-instantiated)
from pyspark.sql import HiveContext; hc = HiveContext(sc)	Not needed — spark session has Hive-compatible SQL support via Delta schemas
from pyspark.sql import SQLContext; sqlc = SQLContext(sc)	Not needed — use spark.sql(...) directly
SparkSession.builder.enableHiveSupport().getOrCreate()	Not needed in Fabric — spark is pre-built and available
sc.textFile("wasb://container@account.blob.core.windows.net/path")	spark.read.text("abfss://workspace@onelake.dfs.fabric.microsoft.com/lh.Lakehouse/Files/path")
sqlContext.sql("CREATE TABLE ... STORED AS ORC")	See hive-to-delta.md for Delta DDL equivalent

In Fabric notebooks, spark (SparkSession) and sc (SparkContext) are pre-instantiated — do not call SparkContext() or SparkSession.builder...getOrCreate() at the top of migrated notebooks.

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Oozie → Fabric Pipelines

Map Oozie workflow actions to Fabric Data Pipeline activities:

Oozie Action Type	Fabric Pipeline Activity	Notes
<spark> action	Notebook activity or Spark Job Definition activity	Pass parameters via notebook cell parameters or SJD arguments
<hive> action	Script activity (SQL) against Lakehouse SQL Endpoint	Convert HiveQL to Spark SQL or Delta SQL
<shell> action	Azure Function activity or Web activity	Shell scripts must be refactored; no direct shell execution in Fabric Pipelines
<java> action	Azure Batch activity (external) or refactor to PySpark	Java MapReduce jobs must be rewritten
<sqoop> action	Copy Data activity (Fabric Data Factory connector)	Sqoop import/export maps to Fabric Copy Data with JDBC source/sink
<coordinator> (time-based schedule)	Pipeline schedule trigger	Set recurrence in pipeline trigger; supports cron-like expressions
<coordinator> (data-triggered)	Storage Event trigger	Trigger on OneLake file arrival

Delegate to spark-authoring-cli for notebook and SJD creation after mapping pipeline activities.

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Introducing notebookutils

HDInsight Spark had no built-in utility framework equivalent to mssparkutils or dbutils. When migrating to Fabric, introduce notebookutils for common operations:

Operation	Old HDInsight Approach	notebookutils Equivalent
List files	dbutils (N/A) / HDFS CLI	notebookutils.fs.ls("abfss://...")
Copy file	HDFS API / shutil	notebookutils.fs.cp(src, dest)
Read secret	Azure Key Vault REST call	notebookutils.credentials.getSecret(keyVaultUrl, secretName)
Get notebook context	Not available	notebookutils.runtime.context — returns workspace ID, notebook ID, etc.
Run child notebook	Not available	notebookutils.notebook.run("notebook_name", timeout, {"param": "value"})
Exit notebook with value	sys.exit()	notebookutils.notebook.exit("value")
Mount storage	WASB config in spark-defaults.conf	OneLake Shortcut (no runtime mount needed)

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Spark Configuration Differences

HDInsight Concept	Fabric Spark Equivalent	Migration Action
spark-defaults.conf (cluster-wide)	Fabric Spark Workspace Settings + Environment item	Move config properties to Environment or use %%configure in notebooks
%%configure magic	%%configure magic — identical	No change needed
YARN queue / resource allocation	Fabric Spark pool node size and autoscale settings	Map queue SLAs to Custom Pool configuration
Ambari service configs (HDFS, YARN tuning)	Not applicable — Fabric manages infrastructure	Remove; focus on application-level Spark configs
HDI Spark version (e.g., Spark 2.4)	Fabric Runtime 1.3 = Spark 3.5 (latest)	Test for deprecated API removals (e.g., HiveContext, RDD-style ML)
Conda environment / bootstrap.sh	Fabric Environment item with custom libraries	Recreate conda/pip dependencies in a Fabric Environment
hive-site.xml (metastore connection)	Not needed — Delta Lake IS the metastore in Fabric	Remove metastore config; use Lakehouse schemas for namespace organization

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Must / Prefer / Avoid

MUST DO

• Replace all wasb:// / wasbs:// paths with OneLake abfss:// paths or OneLake Shortcuts — wasb:// requires storage account keys which are not the Fabric-preferred auth model
• Replace HiveContext, SQLContext, and standalone SparkContext() — use the pre-instantiated spark session in Fabric notebooks
• Migrate Hive DDL (STORED AS ORC, LOCATION, TBLPROPERTIES) to Delta Lake DDL — see hive-to-delta.md
• Introduce notebookutils for file system operations, secret retrieval, and child notebook orchestration where HDInsight used custom scripts or direct API calls
• Replace Oozie XML workflows with Fabric Data Pipelines — see § Oozie → Fabric Pipelines
• Align library management to Fabric Environments — remove bootstrap.sh, conda envs, and runtime %pip install patterns for production workloads

PREFER

• OneLake Shortcuts over copying data — mount existing ADLS Gen2 containers as shortcuts to avoid re-ingestion during migration
• Delta Lake for all tables migrated from Hive ORC/Parquet — ACID guarantees, time travel, and schema enforcement improve data quality
• Fabric Starter Pool for initial migration validation — no pool configuration overhead, fast session startup
• Lakehouse schemas (database namespaces) for organizing migrated Hive databases — one schema per Hive database within a single Lakehouse
• Medallion architecture for restructuring migrated data layers during migration — align Bronze/Silver/Gold with raw Hive → validated Delta → serving Gold patterns

AVOID

• Do not use SparkContext() or HiveContext() constructors in Fabric notebooks — they conflict with the pre-instantiated spark session and will raise errors
• Do not use hive-site.xml or external Hive metastore configuration — Fabric's Delta Lake-backed Lakehouse IS the metastore
• Do not assume YARN queue mappings translate to Fabric pools — re-design resource allocation based on Fabric Spark pool SLAs
• Do not attempt to run Oozie shell actions or Java MapReduce jobs directly in Fabric — these must be refactored (see § Oozie → Fabric Pipelines)
• Do not use %sh magic for file system operations in production notebooks — use notebookutils.fs.* for portability and OneLake token-based auth

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Examples

See code-patterns.md for full before/after examples. Key quick references:

Legacy context → Fabric pre-instantiated session

# HDInsight (remove entirely)
from pyspark.sql import HiveContext
hc = HiveContext(sc)

# Fabric — use pre-instantiated spark directly
df = spark.sql("SELECT * FROM sales.fact_orders")

WASB path → OneLake path (after shortcut creation)

# HDInsight
df = spark.read.parquet("wasb://raw@myaccount.blob.core.windows.net/orders/")

# Fabric
df = spark.read.parquet("Files/raw/orders/")

Hive DDL → Delta DDL

-- HDInsight
CREATE TABLE sales_db.fact_orders (...) STORED AS ORC LOCATION 'wasb://...';

-- Fabric
CREATE SCHEMA IF NOT EXISTS sales_db;
CREATE TABLE sales_db.fact_orders (...) USING DELTA;